Matthew J. Salie, Ph.D. Project Summary: Bacterial cells have evolved to quickly adapt to changing environments to maintain optimal growth. Adaptation requires many specific physiological changes to the transcriptome, proteome, and metabolome of the cell, but changes in the proteome requires the largest energy input. In E. coli, the proteome accounts for nearly 75% of the total cell biomass and it carries all of the metabolic flux, and so a reasonable approximation of the physiological state of the cell can be made by quantifying the proteome. In this proposal, we will use quantitative whole- proteome measurements to learn how bacterial cells can maintain optimal growth under nutrient-limiting conditions. Previous work from our lab demonstrated that the Escherichia coli proteome can be described by six coarse-grained sectors that respond in defined ways to specific limitations of carbon, nitrogen, or ribosomes. The mass fraction of each proteome sector responds in a linear fashion to limitation-based changes in growth rate. Therefore, the proteome composition is predictable given that the growth rate and limitation is known. A remaining challenge is to apply this model to disease states, such as infection by pathogens like Pseudomonas aeruginosa. These pathogens are unaffected by typical antibiotic strategies largely due to their very slow growth rate. However, the current coarse-grained proteome sector model was not parameterized at the very slow growth rates (> 2 h doubling time) characteristic of persistent infections, and it does not include the effect of protein degradation and turnover. In the proposed work, we will expand the sector model by testing three new growth-limiting conditions (sulfur, phosphorous, oxygen), testing a wide range of growth rates (2-24 h doubling times) using a chemostat, and measuring global protein degradation rates using heavy isotope pulse labeling coupled to tandem mass spectrometry. Furthermore, we will perform a direct proteome comparison of E. coli and P. aeruginosa grown in limiting conditions to determine the applicability of the model among different species. Through these studies, we will expand he predictive power of the coarse-grained proteome sector model to better understand bacterial physiology in a disease-relevant setting.